Andrew H. Fagg: Robotics and Machine Learning

Manipulation Skills in Humans and Robots

Skills involving reaching, grasping, and manipulation are a rich focus
of inquiry because they enable both humans and monkeys to affect their
environments in a flexible manner. By studying these motor skills, I
hope to build robots that will be able to perform tasks within
unstructured human environments, as well as environments that are
inhospitable to humans, including space. I am particularly interested
in drawing inspiration for robot control systems from the study of
biological control and in the use of robots as a mechanism in which to
test biological theories of motor control.

One of my projects has been the design and construction of the UMass
Torso robot. As with many humanoid-form robots, the UMass Torso
consists of many controllable degrees-of-freedom and sensors. Thus,
there are often many ways in which a task may be accomplished with the
available sensor and actuator set. Although this design increases the
complexity of the control and sensing problem, the redundancies in
which a task may be addressed can be exploited to allow the robot to
perform a wide range of tasks while optimizing for a variety of task
criteria. The research challenge is to manage these complexities and
provide layers of abstraction that 1) enable a programmer to work at
an intuitive task level, and 2) allow planning and machine learning
algorithms to be used in a practical manner to automatically improve
motor skills (or more specifically, control policies).

Grasping as a Haptic Control Problem

One class of closed-loop controllers that we
have been developing is aimed at the formation of stable grasps.
Rather than starting with a detailed model of the object to be grasped
(e.g., as derived from a vision system), the first step in our
approach is to haptically explore the object to be grasped.
At each contact with the object, the controller estimates the total
force and torque applied to the object by the set of contacts.
Given a simple model of the local object geometry, the controller
computes movements of the fingers and arm that attempt to reduce the
total force and torque.
In particular, we formulated the problem in terms of simultaneously
satisfying the objectives of minimizing the net force and net torque
that are applied to an object.
The power of this approach to grasp formation is that the
controller can be assigned a variety of different physical
resources, including finger tips, palms, multiple hands, and even
``virtual contacts,'' such as gravity.

Learning to Prospectively Select Grasps

The problem of grasping an object and moving it to another location
has long been studied in robotics. One approach to this problem is to
explicitly compute ``pick-and-place'' constraints and to perform a
search within the constrained space. In contrast, humans are capable
of robustly planning and executing grasps to objects about which their
knowledge is incomplete. Furthermore, it appears that grasping
strategies are acquired incrementally as a function of experience with
different objects.

We have applied a reinforcement learning
technique to the problem of discovering an appropriate sequence of
grasp and place actions. Rather than starting with a model of
which grasp was appropriate for a given final object
configuration, the robot learned through interaction with the environment
to select a grip in anticipation of how the grasped object was to be
used in future actions. The behavior exhibited through the learning
process by the robot demonstrated interesting qualitative similarities
to what one sees in grip selection with children in a similar task.